Baselines:   
Aligned to our iterative approach we explore two alternatives as baselines. Seq-SCN: Inspired from Automatic Speech Recognition community [1]}, a naive baseline could be designed where we train an independent Spelling Correction Module (SCM) based on sequence-to-sequence architecture from text-only training data [2]} consisting of paired model-hypothesis and corresponding ground-truth. Deterministic-Feedback: Here, we simply replace the CVAE based feedback module by a deterministic encoder(bi-LSTM)-decoder (parameter prediction network) architecture along with Shi [3]}.
